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AAAI
2006
13 years 5 months ago
Representing Systems with Hidden State
We discuss the problem of finding a good state representation in stochastic systems with observations. We develop a duality theory that generalizes existing work in predictive sta...
Christopher Hundt, Prakash Panangaden, Joelle Pine...
BMCBI
2004
208views more  BMCBI 2004»
13 years 4 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein
RECOMB
2004
Springer
14 years 4 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Keith Noto, Mark Craven
CSL
1999
Springer
13 years 4 months ago
A hidden Markov-model-based trainable speech synthesizer
This paper presents a new approach to speech synthesis in which a set of cross-word decision-tree state-clustered context-dependent hidden Markov models are used to define a set o...
R. E. Donovan, Philip C. Woodland
MLMI
2004
Springer
13 years 9 months ago
Mapping from Speech to Images Using Continuous State Space Models
In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it po...
Tue Lehn-Schiøler, Lars Kai Hansen, Jan Lar...